Supplementary Information A Methodological Assessment and Characterization of Genetically-Driven Variation in Three Human Phosphoproteomes Brett W. Engelmann1,4*†, Chiaowen Joyce Hsiao1†, John D. Blischak1, Yannick Fourne1, Zia Khan1,5, Michael Ford3, Yoav Gilad1,2* 1
Department of Human Genetics, University of Chicago, Chicago, Illinois, USA Department of Medicine, University of Chicago, Chicago, Illinois, USA 3 MS Bioworks, LLC, 3950 Varsity Drive, Ann Arbor, Michigan, USA 4 Current Address: AbbVie, North Chicago, Illinois, USA 5 Current Address: Genentech, South San Francisco, California, USA 2
*
Correspondence to:
[email protected] (B.W.E);
[email protected] (Y.G.) Contribution
†Equal
Supplemental Figure 1. Phosphoproteome summary statistics. (A) Overlapping class one phosphosites across all samples. (B) Number of phosphosites per protein. (C) Mass error of all identified phosphopeptides (median = .1 parts per million). (D) Class one phosphopeptide amino acid distribution. (E) Distribution of class one phosphopeptides with one, two, or three phosphorylated sites. (F) Site level overlap of singly, doubly, and triply phosphorylated class one phosphopeptides. Supplemental Figure 2. Batch effect identification and post-correction exploratory data analysis. (A) PCA of SILAC phosphopeptide ratios. (B) PCA of SILAC phosphopeptide ratios following batch effect correction with ComBat. Supplemental Figure 3. Protein expression dynamic range. Intensity-based absolute quantification (iBAQ) values for the 1,181 protein groups assigned to 3,257 phosphopeptides plotted against their rank. Supplemental Figure 4. Phosphopeptide variance component estimates. Violin plots of (A) absolute and (B) relative phosphopeptide variance components without accounting for protein expression levels. Violin plots of (C) absolute and (D) relative phosphopeptide variance components after accounting for processing date and protein expression levels. Relative variance components were calculated by dividing the absolute value by the sum of all three variance components. Supplemental Figure 5. Volcano plots for each inter-individual comparison. Phosphopeptides categorized as differentially expressed at an FDR of 5% are highlighted in red. Supplemental Figure 6. Amino acid probability logos for differentially expressed phosphopeptides. (A) – (F). Logos are presented for each inter-individual comparison and fold change direction. The position-specific amino acid enrichment odds are calculated relative to the complement of phosphoserine peptides subjected to differential phosphorylation analysis. Supplemental Figure 7. Relationship between SNP proximity and phosphopeptide variability. The primary protein structure ‘distance’ between the nearest non-synonymous variant (in cis) to each phosphopeptide was regressed against nominal F-test P-values. Pearson’s correlation coefficient and P-value inset. A pseudocount of 1 was added to avoid undefined values. Supplemental Figure 8. Relationship between differential phosphorylation power and technical replication. Power to detect differential phosphorylation is shown relative to multiples of observed technical variance. Lines are specific to the number of technical replicates.
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